Image enhancement in optical coherence tomography using deconvolution
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Abstract
Description
This application claims priority under 35 U.S.C. Sec. 119 from Provisional Patent Application, Ser. No. 60/040,798, filed Mar. 17, 1997; the disclosure of which is incorporated herein by reference.
Optical Coherence Tomography (OCT) is a technology that allows for noninvasive, crosssectional optical imaging in biological media with high spatial resolution and high sensitivity. OCT is an extension of lowcoherence or whitelight interferometry, in which a low temporal coherence light source is utilized to obtain precise localization of reflections internal to a probed structure along an optic axis. In OCT, this technique is extended to enable scanning of the probe beam in the direction perpendicular to the optic axis, building up a twodimensional reflectivity dataset, used to create a crosssectional grayscale or falsecolor image of internal tissue backscatter.
OCT has been applied to imaging of biological tissues in vitro and in vivo, although the primary applications of OCT developed to date have been for high resolution imaging of transparent tissues such as the eye. In an OCT image, the detectable intensities of the light reflected from layers of most thick scattering tissues range from 10^{5} to 10^{10} th part of the incident power. For most of these studies, compact and inexpensive super luminescent diode sources (SLDs) have been used as interferometer illumination sources. These commercially available SLDs provide ranging resolutions of 15 to 20 μm (full width at half maximum, i.e., FWHM) in free space. In order to probe ultrastructural details in tissues with fine detail, higher longitudinal resolution is desirable. This is particularly important in the case of noninvasive medical diagnostics, since it is useful to obtain high depth resolution imaging using compact and inexpensive sources which could be easily integrated with endoscopes and catheters.
Epithelial cancers of the breast, lung, and GI tract comprise over 50 percent of all cancers encountered in internal medicine. Many epithelial cancers are preceded by premalignant changes, such as dysplasia or adenoma. Most early GI cancers originate in the superficial layers (i.e., mucosa and submucosa) of the gastrointestinal tract. Because the depth range of OCT imaging is 2 to 3 mm, OCT is sufficient to penetrate superficial tissue layers lining all internal and external free surfaces of the body, including vascular, respiratory, and GI systems, as well as the skin. If axial resolution of OCT images can be optimized to provide cellular resolution (i.e., in the order of 5 μm), OCT could be used in accurate GI cancer staging and high fidelity diagnosis of precancerous diseases such as Barrette's esophagus and chronic ulcerative colitis.
Because the axial resolution is a function of the coherence length of the low coherence source, typically on the order of 15 μm, one known attempt to gain high depth resolution has utilized an ultrashort pulse laser as an alternative source of low coherence length illumination. Ranging resolution of 3.7 μm FWHM has been reported using femtosecond Kerrlens modelocked TI:Al_{2} O_{3} laser illumination. A disadvantage with these femtosecond sources is that they are very complicated and expensive, and their medical usage still remains difficult. Accordingly, a need exists for a system that can achieve the high depth resolution imaging utilizing the compact and inexpensive SLDs which can be easily integrated in endoscopes and catheters.
Another problem in known OCT systems, is the formation of unwanted speckle noise in the final grayscale or falsecolor image. This speckle noise is caused by the existence of closely spaced reflecting or backscattering sites (located within a coherence length of the SLD to each other) within the sample. Speckle is caused by destructive or constructive interference between the waves backscattered from closely spaced reflecting sites. Because prior art OCT systems have detected only the envelope (i.e., the magnitude data) of the interferometric signal, these systems are unable to resolve the interference between the closely spaced reflectance sites, often producing inaccurate positioning of reflections in addition to spurious reflections in the final image. Accordingly a need also exists for a system that can resolve the closely spaced reflectance sites in the sample, so as to substantially eliminate speckle noise in the final image.
The present invention involves an advancement in OCT technology which significantly extends the potential applications of OCT. In particular, the present invention substantially increases the resolution of OCT and also increases the information content of OCT images through coherent signal processing of the OCT interferogram data.
To obtain an improved longitudinal resolution in OCT, a transfer function model has been developed for OCT interaction with target tissue, where the impulse response is interpreted as a description of the actual locations of the reflecting and scattering sites within the tissue. Estimation of the impulse response provides the true axial complex reflectivity profile of the is sample with the equivalent of femtosecond resolution.
In this model, the interaction of OCT with tissue is described as a linear shift invariant (LSI) system. The impulse response of the LSI system is interpreted as a description of the actual locations and amplitudes of scattering sites within the sample arising from index of refraction inhomogenities and particulate scatterers in the sample. An interferogram obtained having the sample replaced with a mirror is known as the autocorrelation function of the source optical wave form. The interferogram obtained with the tissue in the sample arm is the measured output of the LSI system, and is known as the crosscorrelation function of the incident and backscattered pulse. By obtaining an impulse response profile from the output interferometric signal, using the autocorrelation and crosscorrelation functions, a more accurate description of the tissue sample can be obtained.
Accordingly, a method of the present invention involves estimating the impulse response (which is indicative of the actual reflecting and scattering sites within the tissue sample) from the output interferometric signal according to the following steps: (a) acquiring autocorrelation data from the interferometer system with an optical reflector in the sample arm; (b) acquiring crosscorrelation data from the interferometer system having the biological tissue sample in the sample arm; and (c) processing the autocorrelation data and the cross correlation data to produce an optical impulse response of the tissue.
The impulse response may be obtained from the crosscorrelation and autocorrelation data, in step (c) above, by: (d) obtaining an autopower spectrum from the autocorrelation data by performing a Fourier transform on the autocorrelation data; (e) obtaining a crosspower spectrum from the crosscorrelation data by performing a Fourier transform on the crosscorrelation data; (f) obtaining a transfer function of the LSI system by taking the complex conjugate of the ratio of the crosspower spectrum to the autopower spectrum; and (g) obtaining the optical impulse response of the LSI system by performing an inverseFourier transform on the transfer function. For systems that are capable of measuring the crosspower spectra and autopower spectra directly, steps (f) and (g) may be performed on the measured crosspower and autopower spectra to generate the optical impulse response.
In a preferred embodiment, coherent demodulation is used in combination with the above deconvolution technique to resolve closelyspaced reflecting sites in the sample. It is advantageous to demodulate the interferometric signals at center wavenumber k_{0} for achieving quantum noise limited performance. If demodulation is performed outside the computer using an electronic circuit or digital signal processing hardware, complex envelopes of the interferometric signals can be acquired. By utilizing both the magnitude and phase data of the demodulated interferometric signals, the OCT system of the present invention is able to distinguish between closely spaced reflecting sites within the sample. Another advantage of demodulating the interferometric signal is that the demodulated envelopes can be sampled at a lower sampling frequency (than that required for sampling interferograms themselves); and thus, fewer number of samples need to be stored in the computer memory.
The incorporation of demodulation into the deconvolution process is performed according to the following steps: demodulating the autocorrelation and crosscorrelation data to acquire the complex envelopes of the autocorrelation and crosscorrelation data; obtaining an autopower spectrum by performing a Fourier transform on the complex envelope of the autocorrelation data; obtaining a crosspower spectrum by performing a Fourier transform on the crosscorrelation data; obtaining a transfer function of the system by taking the complex conjugate of the ratio of the crosspower spectrum versus the autopower spectrum; and obtaining the optical impulse response by performing an inverseFourier transform on the transfer function.
In an alternate embodiment, the magnitude data obtained from the above demodulation step may be used, discarding the phase data obtained from the demodulation step. Such an embodiment will obtain enhanced OCT images and will still be able to sample at a lower sample frequency.
Additionally, in the above embodiments, it is advantageous to multiply the transfer function, obtained using the deconvolution technique, with a windowing function (such as a Hanning window) to minimize noise and ringing or sidelobes in the impulse response. It is also advantageous, in the above deconvolution techniques, that the complete autocorrelation and crosscorrelation function sequences be measured with submicron accuracy. Therefore, to perform this signal acquisition and processing operation, a data acquisition system has been implemented which is capable of compensating for the inevitable velocity fluctuations in the reference mirror speed; and thus, is capable of capturing interferometric data with high accuracy and high signal to noise ratio. This system thus incorporates a long coherencelength calibration interferometer to accurately monitor the reference arm optical path length.
The calibration interferometer preferably uses a narrowband laser illumination source, such as a HeliumNeon (HeNe) laser or a Distributed Feedback Diode Laser (DFB Diode Laser), for calibration of the reference arm optical path length. At every reference arm position, both the SLD interferogram and calibration interferogram are measured, and the measured calibration interferogram is used to determine the true position of the reference mirror. Using this information, the true SLD interferogram is interpolated. The corrected low coherence interferometric signal is demodulated and filtered using analog electronics or a digital signal processing algorithm to obtain the complete complex envelope of the interferometric signal (both phase and magnitude).
A method for incorporating the narrowband illumination source calibration interferometer into the data acquisition system, so as to compensate for the inevitable velocity fluctuations in the reference mirror speed, includes the steps of: extracting a digital clock signal from the measured calibration interferogram data according to a feature of the interferogram data that is regular in time (such as zero crossings), synchronizing the signal acquisition circuitry of the data acquisition system with the extracted digital clock signal (i.e., using the extracted digital clock signal to drive A/D converters to digitize the signal at regular spatial intervals of the reference arm pathlength).
An alternate method for incorporating the narrowband illumination source calibration interferometer into the data acquisition system includes the steps of: digitizing both the calibration and the SLD interferograms at a sampling rate that is higher than twice the frequency of the calibration interferogram; detecting regular features, corresponding to regular intervals of space, of the calibration interferogram (e.g., zero crossings) using a thresholding or pattern recognition algorithm; and resample the SLD interferogram data at the regular intervals using interpolation routines.
FIG. 1 is a blockdiagram representation of a conventional Michelson interferometer;
FIG. 2 is an interferogram of a cover slip placed on a piece of paper taken by the Michelson interferometer of FIG. 1;
FIG. 3a is a flowdiagram representation of a first embodiment of an OCT image enhancement method of the present invention;
FIG. 3b is a flowdiagram representation of a second embodiment of an OCT image enhancement method of the present invention;
FIG. 3c is a flowdiagram representation of third embodiment of an OCT image enhancement method of the present invention;
FIG. 4a is a blockdiagram representation of an OCT dataacquisition system for performing the first embodiment of the OCT enhancement method of the present invention;
FIG. 4b is a blockdiagram representation of an OCT dataacquisition system for performing the second embodiment of the OCT enhancement method of the present invention;
FIG. 4c is a blockdiagram representation of an OCT dataacquisition system for performing the third embodiment of the OCT enhancement method of the present invention;
FIG. 5 is a flowdiagram representation of processing steps performed by a processing algorithm for use with the present invention;
FIG. 6 is a blockdiagram representation of an alternate OCT dataacquisition system for performing any of the above embodiments of the present invention;
FIG. 7a is a demodulated OCT interferogram depicting two closely spaced glassair interfaces resulting in destructive interference between them;
FIG. 7b is a demodulated OCT interferogram depicting two closely spaced glassair interfaces resulting in constructive interference between them;
FIG. 8a shows magnitudeonly deconvolution of the interferogram of FIG. 7a;
FIG. 8b shows magnitudeonly deconvolution of the interferogram of FIG. 7b;
FIG. 9a shows complex deconvolution of the interferogram of FIG. 7a; and
FIG. 9b shows complex deconvolution of the interferogram of FIG. 7b.
As shown in FIG. 1, a conventional scanning Michelson interferometer can be utilized to obtain the depth resolved measurements of reflectors and scatterers in a sample. A low coherence light source 10 is separated into two beams by a 50/50 beam splitter 16, fifty percent of the light power is transmitted to a sample arm 12 and the remaining fifty percent is directed to a reference arm 14. The sample arm 12 includes a sample probe 18, which focuses the sample beam into the sample 20 and collects the retroreflected light from the sample. The reference arm 14 includes a reference probe 22 which transmits the reference beam onto a retroreflecting mirror 24, translating towards or away from the reference probe, and collects the light retroreflected back from the mirror 24. The retroreflected beams from the sample 20 and mirror 24 are combined again in the beam splitter 16 into a detected electric field 26, which is directed to the optical detector 28. Because a low coherence light source 10 is used, an interferometric signal is produced at the detector 28 when the sample probe path distance to a reflecting or scattering site within the sample 20 matches the reference arm length, to within a source coherence length. For every reflecting or scattering site within the sample, a fringe pattern will appear in the interferometric signal similar to that as shown in FIG. 2. The axial profiles of backscatter versus depth are measured by translating the reference mirror 24 and by synchronously recording the envelope of the interferometric signal at the detector 28. This profile is known as the OCT Ascan of the sample. Two dimensional crosssectional imaging of the sample is performed by laterally scanning the sample probe 18 during successive Ascans. The resulting data set is processed in a computer 30 and displayed as a gray scale or false color image. A series of twodimensional images can be acquired by scanning the probe beam perpendicular to the direction of lateral scanning. The series of twodimensional images can then be rendered into a three dimensional display or a pseudo three dimensional display in gray scale or false color.
Those of ordinary skill in the art will recognize that, although it is preferred to scan the sample probe 18 with respect to the sample 20, the sample may also be scanned with respect to a stationary sample probe. It will also be recognized that the technique of optical coherence domain reflectometry (OCDR) is very similar in nature to OCT. OCDR measures profile of reflectivity as a function of depth in a specimen, and essentially measures OCT Ascans. Accordingly, the inventions described herein are also applicable to OCDR systems and for the purposes of this disclosure, the terms OCT and OCDR will be used interchangeably. It also to be understood, that for the purposes of this disclosure, the term "optical" is to pertain to all ranges of electromagnetic radiation, and preferably pertains to the range of 100 nanometers to 30,000 nanometers.
In developing the present invention, a unique transfer function model has been developed for OCT interaction with the sample, where the impulse response is interpreted as a description of the actual locations of the reflecting and scattering sites within the sample. Based upon this model, The transfer function of the system can be calculated from the source autopower spectrum and the crosspower spectrum of the electric fields in the reference and sample arms. The estimation of the impulse response from the transfer function provides the true axial complex reflectivity profile of the sample with the equivalent of femtosecond temporal resolution.
In this transfer function model, the interaction of OCT with the sample is described as a linear shift invariant (LSI) system. The optical impulse response h(z), where z indicates depth within a specimen, provides deconvolved information regarding actual locations and amplitudes of reflecting and scattering sites within the sample arising from index of refraction inhomogenities and particulate scatters in the sample. The impulse response h(z) is taken from the inverse Fourier transform of the transfer function H(k) (k indicates wavenumber) of the LSI system.
The derivation of the transfer function model is as follows: A source electric field, expressed in scalar form as 2(vtz), is assumed to be incident on the interferometer. We assume that the center wavenumber of the source field or the source power spectrum is k_{0}. Here z is the space variable in both arms, t indicates time and v is the group velocity at the source center frequency. Group velocity dispersion in the sample is assumed to be negligible over a typical scan depth of a few millimeters. l_{r} and l_{s} are the optical path lengths in the reference and sample arms, respectively. Assuming an ideal reference mirror, √2e_{i} (vt2l_{r}) and √2e_{s} (vt2l_{s}) are the fields returning from the reference and sample arms, respectively. Note that e_{i} (vtz)=e_{i} (vtz) exp(j2πk_{0} (ctz)) and e_{s} (vtz)=e_{s} (vtz) exp(j2πk_{0} (ctz))) where c indicates the phase velocity of the wave and j=√1. Here e_{i} and e_{s} represent the complex envelopes of the electric fields, e_{i} (vtz) and e_{s} (vtz), respectively. The detector current is proportional to the temporal average of the detected field power e_{d} ^{2}. Mechanically scanning the reference arm length generates an alternating component of the detector current which is a crosscorrelation of e_{i} and e_{s} and is a function of the roundtrip optical path length difference between the reference and sample arms Δl=2(l_{r} l_{s}).
An interferogram obtained having the sample 20 replaced with a mirror 20' in the sample arm 12, is the "autocorrelation function" (Δl) of the source optical wave form and is treated as an input to the LSI system. The interferogram obtained with the sample 20 in the sample arm 12 is the measured output of the LSI system, known as the "crosscorrelation function" (Δl). The correlation functions are expressed as
R.sub.is (Δl)=<e.sub.i (vt)e.sub.s *(vt+Δl)>, R.sub.ii (Δl)=<e.sub.i (vt)e.sub.i *(vt+Δl)> (1)
where <> denotes averaging over the response time of the detector. According to the WienerKhinchin theorem, the Fourier transforms of the autocorrelation and crosscorrelation functions are the autopower and crosspower spectra denoted by (k) and (k), respectively, where k represents wavenumber.
As discussed above, the impulse response h(z) is interpreted as a description of the actual locations and amplitudes of reflecting and scattering sites within the sample arising from refractive index inhomogeneities and particulate scatterers. The backscattered electric field is given by ##EQU1## where represents convolution. Note that shift invariance allows omission of the terms vt in this expression. The convolution theorem leads to ##EQU2## where (k), (k), and H(k) are the Fourier transforms of (z), (z), and h(z), respectively. H(k) is the system transfer function. Inserting Eq. 2 in Eq. 1 leads to ##EQU3## The superscript * indicates a complex conjugate. Simply put, the autopower and crosspower spectra, (k) and (k), are the Fourier transforms of the crosscorrelation and autocorrelation functions, (Δl) and (Δl), respectively; and the complex conjugate of the transfer function H*(k) is the ratio of the crosspower spectra and the autopower spectra.
In practice, the correlation functions defined in Eq. 1 are hard to measure. The measured (or estimated) correlation functions are influenced by the properties of the optical elements, the measurement electronics, and data acquisition systems, and various noise sources. Therefore what we measure are "estimates" of (Δl) and (Δl). However, for the description of deconvolution algorithms and claims we will still use the symbols (Δl) and (Δl) to indicate the "estimates" of autocorrelation and crosscorrelation functions, respectively. For the purposes of clarity, the terms "autocorrelation and crosscorrelation functions" may be used for the terms, "the estimates of autocorrelation and crosscorrelation functions" in describing the present inventions.
Similarly the measured (or estimated) power spectra are influenced by the properties of the optical elements, the measurement electronics, and data acquisition systems, and various noise sources. Therefore what we measure are "estimates" of (k) and (k), However, for the description of deconvolution algorithms and claims we will still use the symbols (k) and (k), to indicate the "estimates" of autopower and crosspower spectra, respectively. For the purposes of clarity, the terms "autopower and crosspower spectra" may be used for the terms, "the estimates of autopower and crosspower spectra" in describing the present inventions.
In the setups where the group velocity is different than the phase velocity, the method is applicable if v is interpreted as group velocity. Also, while we describe the specific case of a device which uses infrared light source, the deconvolution procedure is applicable to any interferometric device illuminated by any electromagnetic radiation source.
In Eq. 2, we describe the lightspecimen interaction as a linear shift invariant system. We describe the deconvolution methods based on Eq. 5. It should be apparent to a person skilled in the art that the interaction described by Eq. 2 and/or Eq. 4 can be exploited by many other methods in space/time domain including iterative deconvolution methods, CLEAN deconvolution algorithm, etc. This model also forms the basis of "blind" deconvolution methods which do not use a priori information about the autocorrelation function but assume that it convolves with the impulse response.
The true transfer function H(k) is rarely estimated or measured. In most practical cases, what we get is an "estimate" of the transfer function which is different than the true transfer function. One can obtain this estimate in various ways. One such method is taking the ratio of crosspower spectrum and the autopower spectrum and omitting the step of complex conjugation.
Mechanical elements rarely perform a perfect job in scanning the optical path lengths or measuring optical spectra. Since the spectra and correlation functions play a major role in deconvolution, it is useful to have the means to correct for irregularities in the scan rate of the optical path length difference between the reference path and the sample path or the irregularities in measuring the spectra. As will be described below, we have developed a calibration interferometer to achieve such corrections.
Accordingly, based upon the above transfer function model, the present invention provides an access to understanding of interaction of the specimen with the electric fields themselves by performing simple correlation measurements using a Michelson interferometer. The impulse response is estimated from the output interferometric signal according to the steps as illustrated in FIG. 3a. As indicated in step 32, the autocorrelation function (Δl) is acquired from an OCT system having an optical reflector in the sample arm; and as indicated in step 34, the crosscorrelation function (Δl) is acquired from the OCT system having the biological tissue sample in the sample arm. As indicated in step 36, the autopower spectrum (k) is obtained from the autocorrelation data by performing a Fourier transform on the autocorrelation data (Δl); and as indicated in step 38, the crosspower spectrum (k) is obtained from the crosscorrelation data by performing a Fourier transform on the crosscorrelation data (Δl). As indicated in step 40, the transfer function H(k) of the system is given by the complex conjugate of the ratio of the crosspower spectrum (k) versus the autopower spectrum (k). Since the source spectrum has a finite bandwidth, in order to minimize noise in the impulse response, as indicated in step 41, the estimate of the transfer function is multiplied by a Fourier (i.e., frequency) domain windowing function W(k) to minimize noise and ringing or sidelobes in the impulse response. We denote transfer function estimated in this manner by (k). As indicated in step 42, the estimated impulse response (z) is obtained by performing an inverseFourier transform on the estimated transfer function (k).
As will be discussed below, the estimated impulse response data (z) can be transmitted to a computer for further processing. The computer may create a twodimensional crosssectional deconvolved grayscale or falsecolor image of the sample by laterally scanning the sample probe between the acquisition of successive Ascans to obtain a plurality of impulse responses (z), one for each lateral point of the sample. A onedimensional axial reflectivity profile can be obtained for each impulse response (z) by demodulating the impulse response (z) and by plotting the magnitude data therefrom. The magnitudes of the complex envelopes of deconvolved impulse responses (z) could also be estimated by an incoherent envelope detection technique. Finally, the twodimensional image is obtained by aligning the onedimensional interferograms sidebyside in sequence.
As shown in FIG. 4a, an OCT data acquisition system 44 for performing the above method includes a lowcoherence interferometer 46 and, preferably, a calibration interferometer 48. The lowcoherence interferometer includes a light source, such as a superluminescent diode ("SLD") source 50, a fiberoptic source line 52 coupled between the SLD 50 and a fiberoptic beam splitter (such as a 50/50 fiber coupler) 54. The beam splitter separates the light received from the source line 52 into two beams; one transmitted to a sample arm 56 via an optical fiber 58, and the other to a reference arm 60 via an optical fiber 62. The fiber 58 is coupled to a sample probe 64 adapted to focus light to a sample 66 and to receive the light reflected back from the sample 66. The reflected light received back from the sample is transmitted back to the beam splitter 54 via the fiber 58. Preferably, the sample probe 64 has an adjustable focal length, thus allowing the adjustment of the focal spot size.
The fiber 62 is coupled to a reference probe 68 adapted to focus the light received from the fiber 62 to a translating reference mirror 70 (usually mounted on a galvanometer), and to receive the light reflected back from the reference mirror 70. The reflected light received back from the reference mirror is transmitted back to the beam splitter 54 via the fiber 62. The reflected light received by the beam splitter 54, back from both the fiber 58 and fiber 62, is combined and transmitted on the fiberoptic line 72 to the photodetector 74. The photodetector 74 produces an analog signal 75 responsive to the intensity of the incident electric field. An example of a photodetector for use with the present invention is a Model 2011, commercially available from New Focus, Inc., Mountain View, Calif.
The optical path length 76 of the sample arm 56 remains constant, while the optical path length 78 of the reference arm 60 changes with the translation of the reference mirror 70. Because a low coherence light source is used, a fringe pattern (interferometric signal) is produced at the photodetector 74 the optical path length 76 to a reflecting or scattering site within the sample 66 matches the optical path length 78 of the reference arm 60 within a coherence length. Recording the detector current while translating the reference mirror 70 provides interferogram data, which is the optical path length dependent crosscorrelation function (Δl) of the light retroreflected from the reference mirror 70 and the sample 66. Collecting interferogram data for a point on the surface of the sample for one reference mirror cycle is referred to as collecting an "Ascan." The Ascan data provides a onedimensional profile of reflecting and scattering sites of the sample 66 verses depth.
The analog interferogram data signal 75 produced by the photodetector 74, for each Ascan, is sent through deconvolution scheme 80, designed to perform the steps as described above in FIG. 3a. The deconvolution scheme 80 includes an analogtodigital converter 82 for converting the analog interferogram data 75 produced by the photodetector 74 into a digital interferogram signal 84. The digital interferogram signal 84 is sent to a Fourier transform algorithm 86 for obtaining the crosspower spectrum (k) data 88. Fourier transform algorithm for use with the present invention is available in software libraries in commercially available software packages such as LabVIEW supplied by National Instruments, Austin, Tex.
The crosspower spectrum data is then sent to a processing algorithm 90 for calculating the transfer function estimate H'(k) data 92. The processing algorithm 90 is coupled to a memory 94 for storing the autopower spectrum data. To obtain the autopower spectrum data, at some point either before or after measurement of the sample, the sample 66 is replaced by a mirror 66' and the data received by the photodetector 74 is the optical path length dependent autocorrelation function (Δl) of the source light generated from the light retroreflected from the reference mirror 70 and the sample mirror 66'. The analogtodigital converter 82 converts the analog autocorrelation function 75 into a digital signal and the Fourier transform algorithm 86 then obtains the autopower spectrum (k) data 88. When the processing algorithm 90 receives the autopower spectrum (k) data, it stores the data in the memory 94. Accordingly, the processing algorithm 90 will have access to the autopower spectrum (k) for calculating the estimate of the transfer function H(k) as described above.
The estimate of the transfer function H(k) 92 of the system is preferably obtained by the processing algorithm 90 according to a complex conjugate of the ratio of the crosspower spectrum (k) versus the autopower spectrum (k). Since the source spectrum has a finite bandwidth, in order to minimize noise in the impulse response, the estimate of the transfer function is multiplied by a windowing function to minimize noise and ringing or sidelobes in the impulse response. Generally, the windowing function is centered at the wavenumber of the SLD illumination source 50; and is a bellshaped function narrow enough to eliminate the noise and wide enough to achieve the desired resolution. An example of such a windowing function is a Hanning window. The windowing can also be performed by multiplying the window function W(k) and the crosspower spectrum (k) and then dividing the product by the autopower spectrum (k). Alternatively, one can divide W(k) by (k) and then multiply the ratio by (k). Another way of achieving the same result is, dividing the autopower spectrum (k) by W(k) and then using the result to divide the crosspower spectrum (k).
The detailed steps performed by the processing algorithm 90 are shown in FIG. 5. First, as indicated in step 256, the crosspower spectrum is divided by the autopower spectrum; and, as indicated in step 258, the result is complex conjugated. Then, as indicated in step 260, the resulting function is multiplied by a Fourier domain windowing function. We denote transfer function estimated in this manner by (k).
Referring again to FIG. 4a, once the transfer function estimate (k) 92 is calculated, the transfer function estimate (k) data is transmitted to an inverseFourier transform algorithm 96 for obtaining the impulse response estimate (z) data 98 from the transfer function estimate (k) data. An inverse Fourier transform algorithm 96 for use with the present invention is available in commercially available software packages such as LabVIEW supplied by National Instruments, Austin, Tex.
Note that the operations described herein have been performed and tested in software using packages such as LabVIEW and MATLAB. It is also within the scope of the invention that these operations be performed by using hardware DSP devices and circuitry. For example, the Fourier transform algorithm 86, the processing algorithm 90 and the inverseFourier transform algorithm 96 may be performed by hardware devices or circuits specially designed to perform the steps as described above. Such hardware devices or circuits are conventional and thus will be apparent to those of ordinary skill in the art.
The impulse response estimate (z) data 98 is transmitted to a computer 100 for creation of the twodimensional (2D) deconvolved grayscale or falsecolor image data 102. Generally, this includes the steps of: passing the impulse response (z) is through an envelope detector to obtain its envelope, and aligning the envelopes of adjacent impulse response estimates (z) to generate a 2D image. The envelope detection can be performed by various means. One such method is use a coherent demodulator and perform demodulation at wavenumber (i.e., spatial frequency k_{0}) to obtain the complex envelope of (z) and retaining the magnitude of the complex envelope. Intensity of the 2D image can be encoded by various means including grayscale and falsecolor rendering.
The computer 100 also preferably generates the control signals 104 for controlling the above process. For example, the computer may simultaneously control the lateral translation of the sample probe 64 and the translation of the reference mirror 70; and the computer 100 may also provide controls for coordinating the deconvolution scheme 80. Furthermore, it should be apparent to one of ordinary skill in the art, that the computer 100, could contain all or portions of the deconvolution scheme 80, or that the deconvolution scheme could be part of a separate analog or digital circuit, etc.
The complex envelope of an interferogram obtained having the sample 20 replaced with a mirror 20' in the sample arm 12, is the autocorrelation function R_{ii} (Δl) of the complex envelopes of the electric fields. The complex envelope of an interferogram obtained with the sample 20 in the sample arm 12 is the measured output of the LSI system, and is also the crosscorrelation function R_{is} (Δl) of the complex envelopes of the electric fields. Note that R_{ii} (Δl) and R_{is} (Δl) are complex envelopes of (Δl) and R_{is} (Δl), respectively (i.e., (Δl)=R_{is} (Δl) exp(j2πk_{0} Δl), (Δl)=R_{ii} (Δl) exp(j2πk_{0} Δl). The correlation functions are expressed as:
R.sub.is (Δl)=<e.sub.i (vt)e.sub.s *(vt+Δl)>
R.sub.ii (Δl)=<e.sub.i (vt)e.sub.i *(vt+Δl)> (6)
where e_{i} and e_{s} represent the complex envelopes of the electric fields, (i.e., (vtz)=e_{i} exp(j2πk_{0} (ctz)) and (vtz)=e_{s} exp(j2πk_{0} (ctz))) where c indicates the phase velocity of the wave and j=√1. According to the WienerKhinchin theorem, the Fourier transforms of the autocorrelation and crosscorrelation functions are the autopower and crosspower spectra denoted by S_{ii} (k) and S_{is} (k), respectively, where k represents wavenumber. Note that current OCT systems acquire only magnitudes of complex envelopes, i.e., R_{is} (Δl) and R_{ii} (Δl). According to our model, the complex envelope of the backscattered electric field is given by ##EQU4## The convolution theorem leads to
E.sub.s (k)=E.sub.i (k)H(k) (8)
where E_{s} (k) and E_{i} (k) are the Fourier transforms of e_{s} (z) and e_{i} (z), respectively. Inserting Eq. 7 in Eq. 6 leads to ##EQU5##
H*(k)=S.sub.is (k)/S.sub.ii (k) (10)
Simply put, the autopower and crosspower spectra, S_{ii} (k) and S_{is} (k), are the Fourier transforms of the crosscorrelation and autocorrelation functions, R_{ii} (Δl) and R_{is} (Δl), respectively; and the complex conjugate of the estimate of the transfer function H*(k) is the ratio of the crosspower spectra and the autopower spectra.
If only magnitudes of the correlation functions are acquired, then estimates of the power spectra are obtained by taking the Fourier transforms of R_{is} (Δl) and R_{ii} (Δl) denoted by S_{is} ^{m} (k) and S_{is} ^{m} (k), respectively. The transfer function estimated this way is denoted by H^{m} (k) and is given by H^{m} (k)=(S_{is} ^{m} (k)/S_{ii} ^{m} (k))*. Inverse Fourier transforming H^{m} (k) would give an estimate of the impulse response h^{m} (z) which is essentially a sharpened OCT Ascan. We define sharpness improvement as the decrease in the widths of isolated reflections in the Ascans.
Accordingly, the impulse response is estimated from the crosscorrelation function according to the steps as illustrated in FIG. 3b. As indicated in step 220, the autocorrelation function (Δl) of electric fields is acquired from an OCT system having an optical reflector in the sample arm; and, as indicated in step 222, the crosscorrelation function (Δl) is acquired from the OCT system having the biological tissue sample in the sample arm. In steps 224 and 226, the magnitudes of complex envelopes, viz., R_{ii} (Δl) and R_{is} (Δl) are acquired by demodulating the interferometric signals. This envelope detection can be performed by various means. One such method is use a coherent demodulator and perform demodulation at wavenumber (i.e., spatial frequency) k_{0} to obtain the complex envelope of (Δl) (or (Δl)) and retaining the magnitude of the complex envelope.
As indicated in step 228, the autopower spectrum estimate S_{ii} ^{m} (k) is obtained by performing a Fourier transform on R_{ii} (Δl); and, as indicated in step 230, the crosspower spectrum estimate S_{is} ^{m} (k) is obtained by performing a Fourier transform on the crosscorrelation function R_{is} (Δl). As indicated in step 232, the transfer function estimate H^{m} (k) of the system is estimated by taking complex conjugates of the ratio of the crosspower spectrum estimate S_{is} ^{m} (k) versus the autopower spectrum estimate S_{ii} ^{m} (k). Since the source spectrum has a finite bandwidth, in order to minimize noise in the impulse response, as indicated in step 234, the estimate of the transfer function is multiplied by a windowing function to minimize noise and ringing or sidelobes in the impulse response. The windowing can also be performed by multiplying the window function W(k) and the crosspower spectrum S_{is} ^{m} (k) and then dividing the product by the autopower spectrum S_{ii} ^{m} (k). Alternatively, one can divide W(k) by S_{ii} ^{m} (k) and then multiply the ratio by S_{is} ^{m} (k). Another way of achieving the same result is, dividing the autopower spectrum S_{ii} ^{m} (k) by W(k) and then using the result to divide the crosspower spectrum S_{is} ^{m} (k).
As indicated in step 236, the transfer function estimated in this manner is given by H^{m} (k). The estimated impulse response h^{m} (z) is obtained by performing an inverseFourier transform on the estimated transfer function H^{m} (k).
As discussed above, the estimated impulse response data h^{m} (z) can be transmitted to the computer 100 for further processing, such as developing the twodimensional crosssectional deconvolved grayscale or falsecolor image of the sample. The magnitude of h^{m} (z) is used for displaying the image.
As shown in FIG. 4b, an OCT data acquisition system 44' for performing the demodulation technique as described above, and as illustrated in FIG. 3b, is presented. Note that identical numerals pertain to like elements. In this system, the autocorrelations and crosscorrelations are passed through a demodulator 200 to acquire the magnitudes of complex envelopes of the correlation functions. Model SR830 DSP Lockin amplifier commercially available from Stanford Research Systems, Stanford Calif., is an example of such a demodulator. If the correlation functions are digitized before demodulating, they can be demodulated using a digital demodulation algorithm implemented in software.
The magnitude of the analog crosscorrelation of complex envelopes of electric fields signal acquired for each Ascan, is sent through the deconvolution scheme 80', designed to perform the steps as described above in FIG. 3b. The deconvolution scheme includes an analogtodigital converter 82 for converting the analog crosscorrelation data produced by the demodulator into a digital crosscorrelation signal. The digital magnitude only crosscorrelation signal is sent to a Fourier transform algorithm 86 for obtaining the crosspower spectrum estimate S_{is} ^{m} (k). The crosspower spectrum estimate data 88 is then sent to a processing algorithm 90 for calculating the transfer function estimate H^{m} (k) data 92. The processing algorithm is coupled to a memory 94 for storing the autopower spectrum estimate data.
To obtain the autopower spectrum estimate data, at some point in the process, the sample 66 is replaced by a sample mirror 66' and the data provided by the demodulator is the magnitude of the optical path length dependent autocorrelation function R_{ii} (Δl) of the complex envelopes of the electric fields retroreflected from the reference mirror 70 and the sample mirror 66'. The analogtodigital converter 82 converts the analog autocorrelation function R_{ii} (Δl) into a digital signal and the Fourier transform algorithm then obtains the autopower spectrum estimate S_{ii} ^{m} (k) data. When the processing algorithm 90 receives the autopower spectrum estimate S_{is} ^{m} (k) data, it stores the data in the memory 94. Accordingly, the processing algorithm will have access to the autopower spectrum estimate S_{ii} ^{m} (k) for calculating the transfer function estimate H^{m} (k) as described above.
The transfer function estimate H^{m} (k) of the system can be obtained according to a complex conjugate of the ratio of the crosspower spectrum estimate S_{is} ^{m} (k) versus the autopower spectrum estimate S_{ii} ^{m} (k). Since the source spectrum has a finite bandwidth, in order to minimize noise in the impulse response, the estimate of the transfer function is multiplied by a windowing function, such as a Hanning window, to minimize noise and ringing or sidelobes in the impulse response.
We denote the transfer function estimated in this manner by H^{m} (k). Once the transfer function estimate H^{m} (k) 92 is obtained, it is transmitted to an inverseFourier transform algorithm 96 for obtaining the impulse response estimate h^{m} (z). The impulse response estimate h^{m} (z) data 98 is then transmitted to the computer 100 for creation of the deconvolved grayscale or falsecolor image data as described above (Note that the impulse response estimate h^{m} (z) obtained in this manner would be similar in nature to the complex envelope of the impulse response estimate (z) obtained from the correlation functions of electric fields themselves).
As discussed above, the autopower and crosspower spectra, S_{ii} (k) and S_{is} (k), are the Fourier transforms of the crosscorrelation and autocorrelation functions, R_{ii} (Δl) and R_{is} (Δl), respectively; and the complex conjugate of the estimate of the transfer function H*(k) is the ratio of the crosspower spectra and the autopower spectra. By utilizing both the magnitude and phase data of the demodulated correlation functions, the preferred embodiment of the OCT system shown in FIGS. 3c and 4c is able to distinguish between closely spaced reflecting sites within the sample.
As illustrated in FIG. 3c, the impulse response is estimated from the crosscorrelation function according to the following steps: As indicated in step 238, the autocorrelation function (Δl) is acquired from an OCT system having an optical reflector in the sample arm; and, as indicated in step 240, the crosscorrelation function (Δl) is acquired from the OCT system having the biological tissue sample in the sample arm. In steps 242 and 244, the complex envelopes R_{ii} (Δl) and R_{is} (Δl) are acquired by demodulating the interferometric signals. In step 246, the autopower spectrum S_{ii} (k) is obtained by performing a Fourier transform on the complex envelopes of autocorrelation data R_{ii} (Δl); and, in step 248, the crosspower spectrum S_{is} (k) is obtained by performing a Fourier transform on the complex envelope of the crosscorrelation function R_{is} (Δl). As indicated in step 250, the estimate of the transfer function H(k) of the system is taken according to a complex conjugate of the ratio of the crosspower spectrum S_{is} (k) versus the autopower spectrum S_{ii} (k). Since the source spectrum has a finite bandwidth, in order to minimize noise in the impulse response, as indicated in step 252, the estimate of the transfer function is multiplied by a windowing function to minimize noise and ringing or sidelobes in the impulse response. We denote the transfer function estimated in this manner by H(k). The windowing can also be performed by multiplying the window function W(k) and the crosspower spectrum S_{is} (k) and then dividing the product by the autopower spectrum S_{ii} (k). Alternatively, one can divide W(k) by S_{ii} (k) and then multiply the ratio by S_{is} (k). Another way of achieving the same result is, dividing the autopower spectrum S_{ii} (k) by W(k) and then using the result to divide the crosspower spectrum S_{is} (k).
As indicated in step 254, the estimated impulse response h(z) is obtained by performing an inverseFourier transform on the estimated transfer function H(k). As discussed above, the estimated impulse response data h(z) can be transmitted to a computer 100 for further processing, such as generating a twodimensional crosssectional deconvolved grayscale or falsecolor image of the sample.
FIG. 4c shows an OCT data acquisition system 44" for performing the complex demodulation technique illustrated in FIG. 3c. Note that identical numerals pertain to like elements. In this system, the autocorrelation and crosscorrelation interferogram data are passed through a demodulator 210 to acquire the complex envelopes of the correlation functions. Model SR830 DSP Lockin amplifier commercially available from Stanford Research Systems, Stanford Calif., is an example of such a demodulator. If the correlation functions are digitized before demodulating, they can be demodulated using a digital demodulation algorithm implemented in software.
The analog crosscorrelation of complex envelopes of electric fields acquired for each Ascan, is sent through the deconvolution scheme 80", designed to perform the steps as described above in FIG. 3c. The deconvolution scheme includes an analogtodigital converter 82 for converting the analog crosscorrelation data produced by the demodulator into a digital crosscorrelation signal. The digital complex crosscorrelation signal is sent to a Fourier transform algorithm 86 for obtaining the crosspower spectrum S_{is} (k) data 88. The crosspower spectrum data 88 is then sent to a processing algorithm 90 for calculating the transfer function estimate H(k) data 92. The processing algorithm is coupled to a memory 94 for storing the autopower spectrum data. To obtain the autopower spectrum data, at some point in the process, the sample 66 is replaced by a sample mirror 66' and the data provided by the demodulator is the optical path length dependent autocorrelation function R_{ii} (Δl) of the complex envelopes of the electric fields retroreflected from the reference mirror 70 and the sample mirror 66'. The analogtodigital converter 82 converts the analog complex autocorrelation function into a digital signal and the Fourier transform algorithm then obtains the autopower spectrum S_{ii} (k) data. When the processing algorithm receives the autopower spectrum S_{ii} (k) data, it stores the data in the memory 94. Accordingly, the processing algorithm will have access to the autopower spectrum S_{ii} (k) for calculating the transfer function estimate H(k) as described above.
The estimate of the transfer function H(k) of the system can be obtained according to a complex conjugate of the ratio of the crosspower spectrum S_{is} (k) versus the autopower spectrum S_{ii} (k). Since the source spectrum has a finite bandwidth, in order to minimize noise in the impulse response, the estimate of the transfer function is multiplied by a windowing function, such as a Hanning window, to minimize noise and ringing or sidelobes in the impulse response. Generally, the windowing function is centered at dc (i.e., zero wavenumber).
We denote transfer function estimated in this manner by H(k). Once the estimate of the transfer function H(k) is calculated, the transfer function estimate H(k) data 92 is transmitted to an inverseFourier transform algorithm 96 for obtaining the impulse response h(z) data 98 from the transfer function estimate H(k) data. The impulse response h(z) data 98 is transmitted to a computer 100 for creation of the deconvolved grayscale or falsecolor image data as discussed above. The magnitude of h(z) is used for displaying the image.
The above data acquisition system 44" provides for coherent deconvolution of the correlation data, is thus able to correct for interference occurring between light backscattered from reflecting and scattering sites in the sample that are closely spaced relative to the coherence length of the SLD.
It is advantageous, in the above deconvolution scheme, that the autocorrelation and crosscorrelation functions be measured with the submicron accuracy. Therefore, to enhance the accuracy of the low coherence interferogram acquisition, a long coherence length calibration interferometer 48 is incorporated into the system to accurately monitor and compensate for the inevitable velocity fluctuations of the reference mirror 70.
As shown in FIG. 4a, the calibration interferometer 48 includes a longcoherence length, narrowband laser illumination source 106, such as a helium neon (HeNe) laser or a distributed feedback diode laser (DFB diode laser), a reference probe 108, and a sample probe 110. The narrowband illumination source must have a coherence length that is longer than the region (depth) in the sample 66 that is being scanned (for example, the HeNe laser has a coherence length of several meters).
The illumination source 106 transmits to a beam splitter 111, which separates the source signal into two illumination source signals, one being transmitted to the reference probe 108 and the other being transmitted to the sample probe 110. The reference probe 108 focuses its illumination source signal to the mirror 70' mounted on the back of the reference mirror 70 which is mounted on the galvanometer, and the sample probe 110 transmits its illumination source signal to a fixed mirror 112. The interferometer also includes a photodetector 114 for receiving the combination of light reflected back from the reference mirror 70 and the fixed mirror 112, and for producing an analog signal 115 corresponding to the intensity of light received. Because a longcoherence length illumination source 106 is used, the analog interferometric signal 115 produced by the photodetector 114 will be a relatively constant amplitude sinusoidal signal, having a frequency equal to the Doppler shift corresponding to velocity fluctuations in the reference mirror 70 experienced by the electric field in the reference arm.
The analog signal 115 produced by the photodetector 114 is sent to an intervaldetect circuit 116, for detecting features in the signal 115 that are regular in time (such as zero crossings). These features 118 are fed into a clock generator circuit 120 for generating a digital clock source signal 122 for clocking (triggering) the analogtodigital converter device 82 used in the deconvolution scheme 80. Accordingly, the sampling rate of the analogtodigital converter 82 will be synchronized according to the fluctuations in the reference mirror 70 translation velocity detected by the calibration interferometer 48. Examples of intervaldetect and clock generator circuits for use with the present invention include Tektronix 465 oscilloscope, commercially available from Tektronix, inc.
As shown in FIG. 6, an alternate data acquisition system 44* is provided for the incorporation of the calibration interferometer 48. Note that identical numerals used in the drawings correspond to like elements. The alternate data acquisition system 44* includes an analogtodigital converter 126 for digitizing the analog signal 115 produced by the photodetector 114. The digitized calibration signal data 128 is transmitted to a thresholddetect algorithm 130 for detecting regular features, corresponding to regular intervals of space, of the calibration interferogram (e.g., zero crossings). Both the output of the thresholding algorithm 130 and the digitized SLD interferogram data 84 are then sent to an interpolation algorithm 132 for resampling the SLD interferogram data 84 at the regular intervals developed by the thresholding algorithm 130. The resampled interferogram data 134 is then sent to optional digital demodulation and deconvolution algorithm 80' for optional demodulation and deconvolution of the resampled interferogram data 134 to generate the impulse response h(z) data 98', as described above, and that is transmitted to the computer 100' for creation of the deconvolved grayscale or falsecolor image data 102'. Thresholding algorithms or interpolation algorithms were developed by the investigators.
In addition to the resolution improvement advantage of the process of deconvolution, one more advantage is the capability of using ultra highpower illumination sources, which are necessary to perform ultra highspeed OCT imaging. The problems with many highpower illumination sources is that their spectra can be substantially irregular, leading to excessive artifacts in the source spectrum. The advantage of performing coherent deconvolution, as described above, is to get rid of the irregularities of the source power spectrum and apply a customized windowing function to obtain desired shape of the source autocorrelation.
As shown in FIGS. 79, preliminary experimental data illustrates the utility of the complex deconvolution method and system described above. FIG. 7a depicts the amplitude (A) and phase (φ) of a demodulated Ascan of two closely spaced reflecting sites within a sample, where the reflections interfere destructively with each other at the center since the waves reflected from the sites are 180° out of phase with each other (note the phase shift in the demodulated data in the center of the figure). FIG. 7b depicts the amplitude (A) and phase (φ) of a demodulated Ascan of two closely spaced reflecting sites within a sample, where the reflections interfere constructively with each other at the center since the waves reflected from the sites are in phase with each other. Accordingly, the separation between the sites in FIG. 7a and FIG. 7b differ from each other by a quarter wavelength. The above destructive or constructive interference is the cause of "speckle" in OCT images resulting from many closely spaced reflecting sites within a sample.
As shown in FIGS. 8a and 8b, deconvolution using magnitudeonly data provides inaccurate spectral estimation leading to errors in the calculated impulse response in both cases. Note the false zero created in FIG. 8a. As shown in FIGS. 9a and 9b, complex deconvolution provides an accurate reconstruction of the original reflecting sites, notably without the false zero in FIG. 9a.
Embodiments of the invention described above calculate the impulse response by first obtaining crosspower spectra and autopower spectra, then obtaining a transfer function of the system by taking the complex conjugate of the ratio of the crosspower spectra to the autopower spectra, and then obtaining the optical impulse response of the system by performing an inverseFourier transform on the transfer function. The optical impulse response of the system may also be generated without having to compute the autopower and crosspower spectra. In particular, a constrained iterative restoration algorithm may be employed to obtain an estimate of the impulse response h" achieved using prior knowledge of the properties of h. This prior knowledge can be expressed using a constraint operator C.
The demodulation electronics of most priorart OCT systems acquire only the magnitude of the crosscorrelation function, R_{is} (Δl). For magnitudeonly OCT images one could impose the positivity constraint as described in Schafer, R. W., Merserau, R. M., and Richards, M. A., "Constrained Iterative Restoration Algorithms," Proc. IEEE, 1981, 69, pp. 432450. We can write an approximation of Eq. (9), above, as R_{is} (Δl)=R_{ii} (Δl)h'(Δl) where h' is an approximation of h and equals to h for isolated reflections. h' is a positive quantity. Its estimates can be obtained by the method of successive approximations described by: ##EQU6## where l is an iteration counter, λ is a parameter and h_{i} (n) is the Ith estimate of h'. We can choose the initial guess as h_{0} =λR_{is} . The conditions required for the convergence of the iterative algorithm are given by:
0<λ≦2/max(S'.sub.ii (k)), Re[S'.sub.ii (k)]>0.(12)
where S'_{ii} (k) is a Fourier transform of R_{ii} (Δl). Our measurements indicate that for the SLD source in our OCT system Re[S'_{ii} (k)]>0 since R_{ii} (Δl) of the SLD has an approximately Gaussian shape. Since the peak of S'_{ii} (k) is at zero frequency, the maximum of S'_{ii} (k) is just the area under R_{ii} (Δl).
In a preliminary application of the above constrained iterative restoration algorithm, deconvolution on a magnitudeonly OCT image of a fresh onion specimen was performed, leading to striking image enhancement. Sharp boundaries of cellular structure were clearly resolved. The dynamic range (defined as the ratio of the peak of R_{ii} (Δl) and the standard deviation of the magnitude of noise) was decreased by only 2 dB since minimal noise is generated. Ten iterations are sufficient to achieve desired results, however thirty iterations were performed in the preliminary application.
While describing the deconvolution methods of the present invention, we talk about the scanning Michelson interferometer where the reference arm length is mechanically scanned by translating the reference mirror. It is to be understood that the deconvolution methods described herein are applicable to any interferometric device which estimates the correlation functions described above. The deconvolution algorithms are also applicable to any device which measures the autopower spectra and crosspower spectra. Thus, the methods of the present invention are applicable to any device capable of measuring any of the above mentioned quantities whether the device operates in free space or is fiber optically integrated. Also, the methods of the present invention are equally applicable in situations where a measuring device is coupled to an endoscope or a catheter or any other diagnostic instrument.
The impulse response was described above as a function of depth z or time or pathlength difference Δl. It is to be understood that the impulse response can also be estimated using our deconvolution methods as a function of the difference between the optical time delay from the radiation source to the reference reflector (i.e., reference path) and the optical time delay from the radiation source to the sample (i.e., sample path); the difference is denoted by τ=Δl/v.
Although a low temporal coherence source is useful in making the measurements in OCDR and OCT, it is to be understood that a high temporal coherence source can be used with our deconvolution methods of the present invention to improve axial resolution.
Whenever we use a mirror to reflect light and perform measurements such as autocorrelation and crosscorrelation functions, one can achieve the similar results by using any other optical reflector. Also we describe that the autocorrelation function is measured using a strong reflector which replaces the specimen in the sample arm. It should be noted that the autocorrelation can also be measured using a strong reflector which is a part of the specimen itself. The autocorrelation function can also be modeled using the information about the radiation source. It can be also calculated using the knowledge of the source power spectral density. For instance, the inverse Fourier transform of the measured source power spectrum would provide an estimate of the autocorrelation function.
The windowing of the transfer function estimate is similar to filtering the estimate of the transfer function using a filter function such as Wiener filter. These filters are used to eliminate ringing in the image as well as for noise reduction, and thus they have a similar function as the windowing functions. These filters and windows can be applied in frequency domain as well as in space/time domain. In space/time domain the window or filter kernels convolve with the crosscorrelation data in order to achieve the results equivalent to those obtained by multiplication in frequency domain.
Having described the invention in detail and by reference to the drawings, it will be apparent that modification and variations are possible without departing from the scope of the invention as defined in the following claims.
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